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Lecture
Dimensionality Reduction
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Related lectures (28)
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Principal Components: Properties & Applications
Explores principal components, covariance, correlation, choice, and applications in data analysis.
Unsupervised Learning: Principal Component Analysis
Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.
Principal Component Analysis: Dimension Reduction
Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.
Principal Component Analysis: Dimensionality Reduction
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Canonical Correlation Analysis: Overview
Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.
Linear Algebra: Singular Value Decomposition
Delves into singular value decomposition and its applications in linear algebra.
PCA: Directions of Largest Variance
Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.
Unsupervised learning: Young-Eckart-Mirsky theorem and intro to PCA
Introduces the Young-Eckart-Mirsky theorem and PCA for unsupervised learning and data visualization.
Estimating the Term Structure: Principal Component Analysis
Covers Principal Component Analysis for yield curve shape estimation and dimension reduction in interest rate models.
Singular Value Decomposition: Image Compression and Applications
Covers Singular Value Decomposition, focusing on its application in image compression and data representation.
